Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms

Juan Guillermo Lazo Lazo; Marco Antonio G. Dias; Marco Aurélio Cavalcanti Pacheco; Marley Maria Bernardes Rebuzzi Vellasco

doi:10.1007/978-3-540-93000-6_5

Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms

Juan Guillermo Lazo Lazo, Marco Antonio G. Dias, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

This chapter describes, in two parts, the methodology proposed for obtaining an approximation of the real option value and of the optimal decision rule for several project investment options by considering technical and market uncertainty. The first part describes the method which approximates the value of a real option using fuzzy numbers to represent technical uncertainties and known stochastic processes to represent market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) so as to reduce the computational time spent on Monte Carlo simulation runs. The second part describes the method for approximating an optimal decision rule and determining the value of a real option for the case where there are several project investment alternatives (options). This method makes use of a genetic algorithm and of known stochastic processes for representing market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) and with variance reduction techniques.

Original language	English
Title of host publication	Intelligent Systems in Oil Field Development under Uncertainty
Editors	Marco A.C. Pacheco, Marley B.R. Vellasco
Place of Publication	Heidelberg
Publisher	Springer Berlin
Pages	139-186
Number of pages	48
ISBN (Print)	9783540929994
DOIs	https://doi.org/10.1007/978-3-540-93000-6_5
State	Published - 2009
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	183
ISSN (Print)	1860-949X

Bibliographical note

Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.

Access to Document

10.1007/978-3-540-93000-6_5

Cite this

Lazo, J. G. L., Dias, M. A. G., Pacheco, M. A. C., & Vellasco, M. M. B. R. (2009). Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms. In M. A. C. Pacheco, & M. B. R. Vellasco (Eds.), Intelligent Systems in Oil Field Development under Uncertainty (pp. 139-186). (Studies in Computational Intelligence; Vol. 183). Springer Berlin. https://doi.org/10.1007/978-3-540-93000-6_5

Lazo, Juan Guillermo Lazo ; Dias, Marco Antonio G. ; Pacheco, Marco Aurélio Cavalcanti et al. / Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms. Intelligent Systems in Oil Field Development under Uncertainty. editor / Marco A.C. Pacheco ; Marley B.R. Vellasco. Heidelberg : Springer Berlin, 2009. pp. 139-186 (Studies in Computational Intelligence).

@inbook{a743f09266294654a15af9cfe53bc414,

title = "Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms",

abstract = "This chapter describes, in two parts, the methodology proposed for obtaining an approximation of the real option value and of the optimal decision rule for several project investment options by considering technical and market uncertainty. The first part describes the method which approximates the value of a real option using fuzzy numbers to represent technical uncertainties and known stochastic processes to represent market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) so as to reduce the computational time spent on Monte Carlo simulation runs. The second part describes the method for approximating an optimal decision rule and determining the value of a real option for the case where there are several project investment alternatives (options). This method makes use of a genetic algorithm and of known stochastic processes for representing market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) and with variance reduction techniques.",

author = "Lazo, {Juan Guillermo Lazo} and Dias, {Marco Antonio G.} and Pacheco, {Marco Aur{\'e}lio Cavalcanti} and Vellasco, {Marley Maria Bernardes Rebuzzi}",

year = "2009",

doi = "10.1007/978-3-540-93000-6_5",

language = "Ingl{\'e}s",

isbn = "9783540929994",

series = "Studies in Computational Intelligence",

publisher = "Springer Berlin",

pages = "139--186",

editor = "Pacheco, {Marco A.C.} and Vellasco, {Marley B.R.}",

booktitle = "Intelligent Systems in Oil Field Development under Uncertainty",

address = "Alemania",

}

Lazo, JGL, Dias, MAG, Pacheco, MAC & Vellasco, MMBR 2009, Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms. in MAC Pacheco & MBR Vellasco (eds), Intelligent Systems in Oil Field Development under Uncertainty. Studies in Computational Intelligence, vol. 183, Springer Berlin, Heidelberg, pp. 139-186. https://doi.org/10.1007/978-3-540-93000-6_5

Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms. / Lazo, Juan Guillermo Lazo; Dias, Marco Antonio G.; Pacheco, Marco Aurélio Cavalcanti et al.
Intelligent Systems in Oil Field Development under Uncertainty. ed. / Marco A.C. Pacheco; Marley B.R. Vellasco. Heidelberg: Springer Berlin, 2009. p. 139-186 (Studies in Computational Intelligence; Vol. 183).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms

AU - Lazo, Juan Guillermo Lazo

AU - Dias, Marco Antonio G.

AU - Pacheco, Marco Aurélio Cavalcanti

AU - Vellasco, Marley Maria Bernardes Rebuzzi

PY - 2009

Y1 - 2009

N2 - This chapter describes, in two parts, the methodology proposed for obtaining an approximation of the real option value and of the optimal decision rule for several project investment options by considering technical and market uncertainty. The first part describes the method which approximates the value of a real option using fuzzy numbers to represent technical uncertainties and known stochastic processes to represent market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) so as to reduce the computational time spent on Monte Carlo simulation runs. The second part describes the method for approximating an optimal decision rule and determining the value of a real option for the case where there are several project investment alternatives (options). This method makes use of a genetic algorithm and of known stochastic processes for representing market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) and with variance reduction techniques.

AB - This chapter describes, in two parts, the methodology proposed for obtaining an approximation of the real option value and of the optimal decision rule for several project investment options by considering technical and market uncertainty. The first part describes the method which approximates the value of a real option using fuzzy numbers to represent technical uncertainties and known stochastic processes to represent market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) so as to reduce the computational time spent on Monte Carlo simulation runs. The second part describes the method for approximating an optimal decision rule and determining the value of a real option for the case where there are several project investment alternatives (options). This method makes use of a genetic algorithm and of known stochastic processes for representing market uncertainty (commodity prices), which are used in combination with stochastic simulations (Monte Carlo simulation) and with variance reduction techniques.

UR - http://www.scopus.com/inward/record.url?scp=59549084917&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-93000-6_5

DO - 10.1007/978-3-540-93000-6_5

M3 - Capítulo de libro

AN - SCOPUS:59549084917

SN - 9783540929994

T3 - Studies in Computational Intelligence

SP - 139

EP - 186

BT - Intelligent Systems in Oil Field Development under Uncertainty

A2 - Pacheco, Marco A.C.

A2 - Vellasco, Marley B.R.

PB - Springer Berlin

CY - Heidelberg

ER -

Lazo JGL, Dias MAG, Pacheco MAC, Vellasco MMBR. Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms. In Pacheco MAC, Vellasco MBR, editors, Intelligent Systems in Oil Field Development under Uncertainty. Heidelberg: Springer Berlin. 2009. p. 139-186. (Studies in Computational Intelligence). doi: 10.1007/978-3-540-93000-6_5

Real option value calculation by Monte Carlo simulation and approximation by fuzzy numbers and genetic algorithms

Abstract

Publication series

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this